Premium
From object detection to text detection and recognition: A brief evolution history of optical character recognition
Author(s) -
Wang Haifeng,
Pan Changzai,
Guo Xiao,
Ji Chunlin,
Deng Ke
Publication year - 2021
Publication title -
wiley interdisciplinary reviews: computational statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.693
H-Index - 38
eISSN - 1939-0068
pISSN - 1939-5108
DOI - 10.1002/wics.1547
Subject(s) - computer science , artificial intelligence , deep learning , optical character recognition , inference , path (computing) , statistical inference , statistical model , machine learning , data science , character (mathematics) , pattern recognition (psychology) , image (mathematics) , geometry , mathematics , statistics , programming language
Text detection and recognition, which is also known as optical character recognition (OCR), is an active research area under quick development with a lot of exciting applications. Deep‐learning‐based methods represent the state‐of‐art of this area. However, these methods are largely deterministic: they give a deterministic output for each input. For both statisticians and general users, methods supporting uncertainty inference are of great appeal, leaving rich research opportunities to incorporate statistical models and methods with the established deep‐learning‐based approaches. In this paper, we provide a comprehensive review of the evolution history of research development on OCR with discussions on the statistical insights behind these developments and potential directions to enhance the current methods with statistical approaches. We hope this article can serve as a useful guidebook for statisticians who are seeking for a path toward edge‐cutting research in this exciting area. This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Deep Learning Data: Types and Structure > Image and Spatial Data